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parts of the region. The link to the tropical oceans has been used as a basis for statistical correction of model output, which has been shown to increase predictive skill for seasonal winter precipitation forecasts in northeastern Afghanistan, northern Pakistan, and the northern tier of the domain (Uzbekistan, Tajikistan, and Kyrgyzstan; Tippett et al. 2003 , 2005 ). The degree to which model improvements could extend the area of predictability or ocean information could be used directly for
parts of the region. The link to the tropical oceans has been used as a basis for statistical correction of model output, which has been shown to increase predictive skill for seasonal winter precipitation forecasts in northeastern Afghanistan, northern Pakistan, and the northern tier of the domain (Uzbekistan, Tajikistan, and Kyrgyzstan; Tippett et al. 2003 , 2005 ). The degree to which model improvements could extend the area of predictability or ocean information could be used directly for
) and aerosol–radiative forcings ( Kim et al. 2010 ). These effects can potentially interact with each other. For example, the variability of land surface conditions can affect the circulation over the ocean, which in turn can modify the SSTs and indirectly affect conditions over land ( Ma et al. 2013 ). The existence of significant impacts on WAM rainfall of slowly varying climate subcomponents indicates the potential for useful long-range forecasts ( Vellinga et al. 2013 ; Gaetani and Mohino 2013
) and aerosol–radiative forcings ( Kim et al. 2010 ). These effects can potentially interact with each other. For example, the variability of land surface conditions can affect the circulation over the ocean, which in turn can modify the SSTs and indirectly affect conditions over land ( Ma et al. 2013 ). The existence of significant impacts on WAM rainfall of slowly varying climate subcomponents indicates the potential for useful long-range forecasts ( Vellinga et al. 2013 ; Gaetani and Mohino 2013
indicating that a more realistic representation of surface conditions reduces model biases, many current numerical models, particularly those used for operational forecasts, still employ fixed land-cover types. Hence, they are unable to represent the additional sources of interannual variability owing to land-cover changes, as a result of either land-use changes or the vegetation’s degree of stress (e.g., during droughts, wet periods, or insect outbreaks). In other words, models that do not include
indicating that a more realistic representation of surface conditions reduces model biases, many current numerical models, particularly those used for operational forecasts, still employ fixed land-cover types. Hence, they are unable to represent the additional sources of interannual variability owing to land-cover changes, as a result of either land-use changes or the vegetation’s degree of stress (e.g., during droughts, wet periods, or insect outbreaks). In other words, models that do not include
topography and vegetation types differ greatly among these climatic zones, the distribution of precipitation is inhomogeneous. It is difficult to use one drought index to reasonably represent all features of droughts over the whole of East Asia. In northwest China, which is an arid/semiarid area, no drought index is able to represent the drought types and severity levels ( J. Wang et al. 2007 ). Great efforts have been devoted to monitoring, understanding, and forecasting drought over East Asia. The
topography and vegetation types differ greatly among these climatic zones, the distribution of precipitation is inhomogeneous. It is difficult to use one drought index to reasonably represent all features of droughts over the whole of East Asia. In northwest China, which is an arid/semiarid area, no drought index is able to represent the drought types and severity levels ( J. Wang et al. 2007 ). Great efforts have been devoted to monitoring, understanding, and forecasting drought over East Asia. The
warm tropical North Atlantic can help define the shape and intensity of the drought episodes ( Seager et al. 2010 ; Mo and Berbery 2011 ). Notably, the effect of land surface–atmosphere interactions, in the form of soil moisture–precipitation coupling, is essential in the development of drought in southern South America ( Xue et al. 2006 ; Wang et al. 2007 ; Ma et al. 2010 ; Sörensson and Menéndez 2011 ). Barreiro and Diaz (2011) noted that improved seasonal forecasts over South America
warm tropical North Atlantic can help define the shape and intensity of the drought episodes ( Seager et al. 2010 ; Mo and Berbery 2011 ). Notably, the effect of land surface–atmosphere interactions, in the form of soil moisture–precipitation coupling, is essential in the development of drought in southern South America ( Xue et al. 2006 ; Wang et al. 2007 ; Ma et al. 2010 ; Sörensson and Menéndez 2011 ). Barreiro and Diaz (2011) noted that improved seasonal forecasts over South America
deleterious consequences it is not surprising that across the Greater Horn, where rain-fed agriculture is the mainstay, where food security is often threatened ( Funk et al. 2008 ; Funk and Brown 2009 ), and where the largest contribution to electricity generation is hydropower ( Kaunda et al. 2012 ), that drought information is especially valued. Nor is the interest in drought information limited to assessments of current conditions or the provision of seasonal forecasts. Seemingly contradictory signals
deleterious consequences it is not surprising that across the Greater Horn, where rain-fed agriculture is the mainstay, where food security is often threatened ( Funk et al. 2008 ; Funk and Brown 2009 ), and where the largest contribution to electricity generation is hydropower ( Kaunda et al. 2012 ), that drought information is especially valued. Nor is the interest in drought information limited to assessments of current conditions or the provision of seasonal forecasts. Seemingly contradictory signals
trends (1979–2012) In this section, we utilize numerical simulations to provide further insight into the nature of recent variability and trends over Eurasia. These simulations take the form of full global reanalyses [MERRA and the Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim)], Atmospheric Model Intercomparison Project (AMIP)-style simulations using the Global Modeling and Assimilation Office (GMAO) GEOS-5 system, and simulations with more idealized SST
trends (1979–2012) In this section, we utilize numerical simulations to provide further insight into the nature of recent variability and trends over Eurasia. These simulations take the form of full global reanalyses [MERRA and the Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim)], Atmospheric Model Intercomparison Project (AMIP)-style simulations using the Global Modeling and Assimilation Office (GMAO) GEOS-5 system, and simulations with more idealized SST
forced by anomalous heat sources over the warm tropical Pacific SST anomalies ( Hoskins and Karoly 1981 ). Trenberth et al. (1988) then applied linear wave theory to link the 1988 drought to the ongoing La Niña event and Palmer and Brankovic (1989) claimed to be able to produce important elements of the same drought within the European Centre for Medium-Range Weather Forecasts (ECMWF) numerical weather prediction model when forced by the observed SSTs. Explaining a seasonal drought is good
forced by anomalous heat sources over the warm tropical Pacific SST anomalies ( Hoskins and Karoly 1981 ). Trenberth et al. (1988) then applied linear wave theory to link the 1988 drought to the ongoing La Niña event and Palmer and Brankovic (1989) claimed to be able to produce important elements of the same drought within the European Centre for Medium-Range Weather Forecasts (ECMWF) numerical weather prediction model when forced by the observed SSTs. Explaining a seasonal drought is good
